1. Technical Field
The present invention relates to a method, computer system, and computer program product for determining disulphide bond connectivity in protein.
2. Related Art
Fariselli and Casadio approached problem of predicting disulphide connectivity by equating the problem to a maximum graph matching problem and assigning edge weights based on the residues in the nearest neighborhood of the cysteines. Weights derived by monte carlo simulated annealing in combination with Edmond-Gabow maximum weighted graph matching algorithm was found to be giving best prediction accuracies for Fariselli and Casadio. See Piero Fariselli and Rita Casadio, “Prediction of disulphide connectivity to proteins”, BioInformatics, Vol 17 No. 10 (2001).
In addition, neural network methods have been used to determine the disulphide bond connectivity of protein. However, a neural network method has the disadvantage of requiring time and effort to train data, and the accuracy the determination of disulphide connectivity of proteins depends on the amount and quality of the data that is trained. Thus, there is a need for a method for determining disulphide bond connectivity in protein that is capable of achieving high accuracy without a need to train data.